Data Science in Practice

Day 2: Data Visualization

1 Data Visualization

This chapter starts with basic principles for data programming or coding involving data. Data programming is a practice that works and evolves with data. Data programming or coding allows the user to manage and process data in more effective manner. Programs are designed to be replicated or replicable by user and collaborators. A data program can be developed and updated iteratively and incrementally. In other words, it is building on the culminated works without repeating the steps. It takes debugging, which is the process of identifying problems (bugs) but, in fact, updating the program in different situations or with different inputs when used in different contexts, including the programmer himself or herself working in future times.

1.1 Principles of Programming

Social scientists Gentzkow and Shapiro (2014) list out some principles for data programming.

1. Automation

• For replicability (future-proof, for the future you)

2. Version Control

• Allow evolution and updated edition
• Use Git and GitHub

3. Directories/Modularity

• Organize by functions and data chunks

4. Keys

• Index variable (relational)

5. Abstraction

• KISS (Keep in short and simple)

6. Documentation

• Comments for communicating to later users

7. Management

• Collaboration ready

1.2 Functionalities of Data Programs

A data program can provide or perform :

1. Documentation of data
2. Importing and exporting data
3. Management of data
4. Visualization of data
5. Data models

1.3 Data programming in R

R basics

# Create variables composed of random numbers
x <-rnorm(50) 
y = rnorm(x)

# Plot the points in the plane 
plot(x, y)

Using R packages

# Plot better, using the ggplot2 package 
## Prerequisite: install and load the ggplot2 package
## install.packages("ggplot2")
library(ggplot2)
qplot(x,y)

1.4 Data Visualization with R

# Plot better better with ggplot2
x <-rnorm(50) 
y = rnorm(x)
ggplot(,aes(x,y)) + theme_bw() + geom_point(col="blue")

1.4.1 Hands-on workshop I: Visualize Taiwan election data

In this section, we demonstrate exploring data about Taiwan elections in 2016. The Taiwan Election and Democratization Study (TEDS) is one of the longest and most comprehensive elections studies starting in 2001. TEDS collects data through different modes of surveys including face-to-face interviews, telephone interviews and internet surveys. More detail of TEDS can be found at the National Chengchi University Election Study Center website at https://esc.nccu.edu.tw/main.php.

Taiwan Election and Democratization Study 2016 data

# Import the TEDS 2016 data in Stata format using the haven package
##install.packages("haven")

library(haven)
TEDS_2016 <- haven::read_stata("https://github.com/datageneration/home/blob/master/DataProgramming/data/TEDS_2016.dta?raw=true")

# Prepare the analyze the Party ID variable 
# Assign label to the values (1=KMT, 2=DPP, 3=NP, 4=PFP, 5=TSU, 6=NPP, 7="NA")

TEDS_2016$PartyID <- factor(TEDS_2016$PartyID, labels=c("KMT","DPP","NP","PFP", "TSU", "NPP","NA"))

Take a look at the variable:

# Check the variable
attach(TEDS_2016)
head(PartyID)

## [1] NA  NA  KMT NA  NA  DPP
## Levels: KMT DPP NP PFP TSU NPP NA

tail(PartyID)

## [1] NA  NA  DPP NA  NA  NA 
## Levels: KMT DPP NP PFP TSU NPP NA

Frequency table:

# Run a frequency table of the Party ID variable using the descr package
## install.packages("descr")
library(descr)
freq(TEDS_2016$PartyID)

## TEDS_2016$PartyID 
##       Frequency  Percent
## KMT         388  22.9586
## DPP         591  34.9704
## NP            3   0.1775
## PFP          32   1.8935
## TSU           5   0.2959
## NPP          43   2.5444
## NA          628  37.1598
## Total      1690 100.0000

Get a better chart of the Party ID variable:

# Plot the Party ID variable
library(ggplot2)
ggplot(TEDS_2016, aes(PartyID)) + 
  geom_bar()

We can attend to more detail of the chart, such as adding labels to x and y axes, and calculating the percentage instead of counts.

ggplot2::ggplot(TEDS_2016, aes(PartyID)) + 
  geom_bar(aes(y = (..count..)/sum(..count..))) + 
  scale_y_continuous(labels=scales::percent) +
  ylab("Party Support (%)") + 
  xlab("Taiwan Political Parties")

Adding colors, with another theme:

ggplot2::ggplot(TEDS_2016, aes(PartyID)) + 
  geom_bar(aes(y = (..count..)/sum(..count..),fill=PartyID)) + 
  scale_y_continuous(labels=scales::percent) +
  ylab("Party Support (%)") + 
  xlab("Taiwan Political Parties") +
  theme_bw()

Hold on, colors are not right!

ggplot2::ggplot(TEDS_2016, aes(PartyID)) + 
  geom_bar(aes(y = (..count..)/sum(..count..),fill=PartyID)) + 
  scale_y_continuous(labels=scales::percent) +
  ylab("Party Support (%)") + 
  xlab("Taiwan Political Parties") +
  theme_bw() +
  scale_fill_manual(values=c("steel blue","forestgreen","khaki1","orange","goldenrod","yellow","grey"))

To make the chart more meaningful, we can use a package called tidyverse to manage the data.

##install.packages("tidyverse")
library(tidyverse)
TEDS_2016 %>% 
  count(PartyID) %>% 
  mutate(perc = n / nrow(TEDS_2016)) -> T2
ggplot2::ggplot(T2, aes(x = reorder(PartyID, -perc),y = perc,fill=PartyID)) + 
  geom_bar(stat = "identity") +
  ylab("Party Support (%)") + 
  xlab("Taiwan Political Parties") +
  theme_bw() +
  scale_fill_manual(values=c("steel blue","forestgreen","khaki1","orange","goldenrod","yellow","grey"))

1.4.2 Hands-on workshop II: Visualize Hans Rosling’s famous gapminder chart

In this section, we replicate Dr. Hans Rosling’s well-renowned animated chart depicting world development over time. For more detail, watch this BBC video. Data are drawn from Gapfinder, a foundation established by the Rosling family.

my_packages <- c("tidyverse", "png","gifski", "gapminder", "ggplot2","gganimate","RColorBrewer")
install.packages(my_packages, repos = "http://cran.rstudio.com")

## 
## The downloaded binary packages are in
##  /var/folders/qp/s6y46pq11y13t0gpnf4_v9vm0000gp/T//Rtmp15vADV/downloaded_packages

library(gapminder)
library(ggplot2)
library(gganimate)
library(gifski)
library(png)
library(RColorBrewer)

data("gapminder")
# Basic scatter plot object

mapping <- aes(x =gdpPercap, y = lifeExp, 
               size = pop, color = continent,
               frame = year) 

# Note: manual color choices.

ggplot(gapminder, mapping = mapping) +
  geom_point() +
  theme_linedraw() + 
  scale_x_log10() +
  scale_color_manual(values=c("darkviolet","darkblue","firebrick1","forestgreen","deepskyblue1")) +
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  geom_text(aes(label=ifelse((country == "China"), "China", ifelse(country=="United States", "United States", ""))),vjust=0,nudge_y = 1,size=6) +
  transition_time(year) +
  ease_aes('linear')

## Exercise
# 1. Check the variables in gapminder
# 2. Change the variable gdpPercap to size and pop to x
# 3. Change the color palette using [RColorBrewer](https://www.nceas.ucsb.edu/sites/default/files/2020-04/colorPaletteCheatsheet.pdf)

1.5 Recommended R Resources:

• The R Journal
• Introduction to R by W. N. Venables, D. M. Smith and the R Core Team
• Introduction to R Seminar at UCLA
• Getting Started in Data Analysis using Stata and R by Data and Statistical Services, Princeton University